{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最简单的模型，只用一个节点\n",
    "\n",
    "import os\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # 不使用GPU\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.models import load_model\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers.core import Dropout\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "# import icecream.ic as ic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "\n",
    "\n",
    "X_train = pd.read_csv('X_train.csv').values\n",
    "Y_train = pd.read_csv('Y_train.csv').values\n",
    "# X_train = X_train.astype('float32')\n",
    "# Y_train = Y_train.astype('float32')\n",
    "\n",
    "X_test = pd.read_csv('X_test.csv').values\n",
    "Y_test = pd.read_csv('Y_test.csv').values\n",
    "# X_test = X_train.astype('float32')\n",
    "# Y_test = Y_train.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' 可以不做\n",
    "# ic(X_train.max())\n",
    "print('max: ', X_train.max())\n",
    "X_train /= X_train.max()\n",
    "# Y_train = to_categorical(Y_train, 2)\n",
    "X_test /= X_test.max()\n",
    "# Y_test = to_categorical(Y_test, 2)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cce2d2f9a0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "plt.plot(X_train[:, 0], X_train[:, 1], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.compat.v1.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.compat.v1.Session(config=config)\n",
    "# K.set_session(sess)\n",
    "tf.compat.v1.keras.backend.set_session(sess)\n",
    "\n",
    "file = 'playground1'\n",
    "if os.path.exists(file+'.h5'):\n",
    "    model = load_model(file+'.h5')\n",
    "    # os.rename(file+)\n",
    "else:\n",
    "    model = Sequential()\n",
    "    model.add(Dense(input_dim=2, units=1, activation='sigmoid'))\n",
    "    # model.add(Dense(1, activation='relu'))\n",
    "    # model.add(Dense(2, activation='softmax'))\n",
    "    # model.add(Dropout(0.2))\n",
    "\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='rmsprop',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "1/1 [==============================] - 1s 674ms/step - loss: 0.1839 - accuracy: 0.9600\n",
      "Epoch 2/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1837 - accuracy: 0.9600\n",
      "Epoch 3/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1835 - accuracy: 0.9600\n",
      "Epoch 4/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1833 - accuracy: 0.9600\n",
      "Epoch 5/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1831 - accuracy: 0.9600\n",
      "Epoch 6/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1829 - accuracy: 0.9600\n",
      "Epoch 7/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1828 - accuracy: 0.9600\n",
      "Epoch 8/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1826 - accuracy: 0.9600\n",
      "Epoch 9/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1824 - accuracy: 0.9600\n",
      "Epoch 10/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1822 - accuracy: 0.9600\n",
      "Epoch 11/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1820 - accuracy: 0.9600\n",
      "Epoch 12/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1818 - accuracy: 0.9600\n",
      "Epoch 13/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1816 - accuracy: 0.9600\n",
      "Epoch 14/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1814 - accuracy: 0.9600\n",
      "Epoch 15/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.1813 - accuracy: 0.9700\n",
      "Epoch 16/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1811 - accuracy: 0.9800\n",
      "Epoch 17/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1809 - accuracy: 0.9800\n",
      "Epoch 18/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1807 - accuracy: 0.9800\n",
      "Epoch 19/1000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
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    },
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    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1038 - accuracy: 0.9900\n",
      "Epoch 654/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 735/1000\n",
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      "1/1 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.99 - 0s 4ms/step - loss: 0.0945 - accuracy: 0.9900\n",
      "Epoch 772/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0912 - accuracy: 0.9900\n",
      "Epoch 817/1000\n",
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      "Epoch 888/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0861 - accuracy: 1.0000\n",
      "Epoch 889/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0861 - accuracy: 1.0000\n",
      "Epoch 890/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0860 - accuracy: 1.0000\n",
      "Epoch 891/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0859 - accuracy: 1.0000\n",
      "Epoch 892/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0859 - accuracy: 1.0000\n",
      "Epoch 893/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0858 - accuracy: 1.0000\n",
      "Epoch 894/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0857 - accuracy: 1.0000\n",
      "Epoch 895/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0857 - accuracy: 1.0000\n",
      "Epoch 896/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0856 - accuracy: 1.0000\n",
      "Epoch 897/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0855 - accuracy: 1.0000\n",
      "Epoch 898/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0855 - accuracy: 1.0000\n",
      "Epoch 899/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0854 - accuracy: 1.0000\n",
      "Epoch 900/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0853 - accuracy: 1.0000\n",
      "Epoch 901/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0853 - accuracy: 1.0000\n",
      "Epoch 902/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0852 - accuracy: 1.0000\n",
      "Epoch 903/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0851 - accuracy: 1.0000\n",
      "Epoch 904/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0851 - accuracy: 1.0000\n",
      "Epoch 905/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0850 - accuracy: 1.0000\n",
      "Epoch 906/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0849 - accuracy: 1.0000\n",
      "Epoch 907/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0849 - accuracy: 1.0000\n",
      "Epoch 908/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0848 - accuracy: 1.0000\n",
      "Epoch 909/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0847 - accuracy: 1.0000\n",
      "Epoch 910/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0847 - accuracy: 1.0000\n",
      "Epoch 911/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0846 - accuracy: 1.0000\n",
      "Epoch 912/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0845 - accuracy: 1.0000\n",
      "Epoch 913/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0845 - accuracy: 1.0000\n",
      "Epoch 914/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0844 - accuracy: 1.0000\n",
      "Epoch 915/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0843 - accuracy: 1.0000\n",
      "Epoch 916/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0843 - accuracy: 1.0000\n",
      "Epoch 917/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0842 - accuracy: 1.0000\n",
      "Epoch 918/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0841 - accuracy: 1.0000\n",
      "Epoch 919/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0840 - accuracy: 1.0000\n",
      "Epoch 920/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0840 - accuracy: 1.0000\n",
      "Epoch 921/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0839 - accuracy: 1.0000\n",
      "Epoch 922/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0838 - accuracy: 1.0000\n",
      "Epoch 923/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0838 - accuracy: 1.0000\n",
      "Epoch 924/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0837 - accuracy: 1.0000\n",
      "Epoch 925/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0836 - accuracy: 1.0000\n",
      "Epoch 926/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0836 - accuracy: 1.0000\n",
      "Epoch 927/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0835 - accuracy: 1.0000\n",
      "Epoch 928/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0834 - accuracy: 1.0000\n",
      "Epoch 929/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0834 - accuracy: 1.0000\n",
      "Epoch 930/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0833 - accuracy: 1.0000\n",
      "Epoch 931/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0833 - accuracy: 1.0000\n",
      "Epoch 932/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0832 - accuracy: 1.0000\n",
      "Epoch 933/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0831 - accuracy: 1.0000\n",
      "Epoch 934/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0831 - accuracy: 1.0000\n",
      "Epoch 935/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0830 - accuracy: 1.0000\n",
      "Epoch 936/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0829 - accuracy: 1.0000\n",
      "Epoch 937/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0829 - accuracy: 1.0000\n",
      "Epoch 938/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0828 - accuracy: 1.0000\n",
      "Epoch 939/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0827 - accuracy: 1.0000\n",
      "Epoch 940/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0827 - accuracy: 1.0000\n",
      "Epoch 941/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0826 - accuracy: 1.0000\n",
      "Epoch 942/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0825 - accuracy: 1.0000\n",
      "Epoch 943/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0825 - accuracy: 1.0000\n",
      "Epoch 944/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0824 - accuracy: 1.0000\n",
      "Epoch 945/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0823 - accuracy: 1.0000\n",
      "Epoch 946/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0823 - accuracy: 1.0000\n",
      "Epoch 947/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0822 - accuracy: 1.0000\n",
      "Epoch 948/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0821 - accuracy: 1.0000\n",
      "Epoch 949/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0821 - accuracy: 1.0000\n",
      "Epoch 950/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0820 - accuracy: 1.0000\n",
      "Epoch 951/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0819 - accuracy: 1.0000\n",
      "Epoch 952/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0819 - accuracy: 1.0000\n",
      "Epoch 953/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0818 - accuracy: 1.0000\n",
      "Epoch 954/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0817 - accuracy: 1.0000\n",
      "Epoch 955/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0817 - accuracy: 1.0000\n",
      "Epoch 956/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0816 - accuracy: 1.0000\n",
      "Epoch 957/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0815 - accuracy: 1.0000\n",
      "Epoch 958/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0815 - accuracy: 1.0000\n",
      "Epoch 959/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0814 - accuracy: 1.0000\n",
      "Epoch 960/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0813 - accuracy: 1.0000\n",
      "Epoch 961/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0813 - accuracy: 1.0000\n",
      "Epoch 962/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0812 - accuracy: 1.0000\n",
      "Epoch 963/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0812 - accuracy: 1.0000\n",
      "Epoch 964/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0811 - accuracy: 1.0000\n",
      "Epoch 965/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0810 - accuracy: 1.0000\n",
      "Epoch 966/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0810 - accuracy: 1.0000\n",
      "Epoch 967/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0809 - accuracy: 1.0000\n",
      "Epoch 968/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0808 - accuracy: 1.0000\n",
      "Epoch 969/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0808 - accuracy: 1.0000\n",
      "Epoch 970/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0807 - accuracy: 1.0000\n",
      "Epoch 971/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0806 - accuracy: 1.0000\n",
      "Epoch 972/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0806 - accuracy: 1.0000\n",
      "Epoch 973/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0805 - accuracy: 1.0000\n",
      "Epoch 974/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0804 - accuracy: 1.0000\n",
      "Epoch 975/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0804 - accuracy: 1.0000\n",
      "Epoch 976/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0803 - accuracy: 1.0000\n",
      "Epoch 977/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0803 - accuracy: 1.0000\n",
      "Epoch 978/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0802 - accuracy: 1.0000\n",
      "Epoch 979/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0801 - accuracy: 1.0000\n",
      "Epoch 980/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0801 - accuracy: 1.0000\n",
      "Epoch 981/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0800 - accuracy: 1.0000\n",
      "Epoch 982/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0799 - accuracy: 1.0000\n",
      "Epoch 983/1000\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0799 - accuracy: 1.0000\n",
      "Epoch 984/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0798 - accuracy: 1.0000\n",
      "Epoch 985/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0797 - accuracy: 1.0000\n",
      "Epoch 986/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0797 - accuracy: 1.0000\n",
      "Epoch 987/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0796 - accuracy: 1.0000\n",
      "Epoch 988/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0796 - accuracy: 1.0000\n",
      "Epoch 989/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0795 - accuracy: 1.0000\n",
      "Epoch 990/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0794 - accuracy: 1.0000\n",
      "Epoch 991/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0794 - accuracy: 1.0000\n",
      "Epoch 992/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0793 - accuracy: 1.0000\n",
      "Epoch 993/1000\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0792 - accuracy: 1.0000\n",
      "Epoch 994/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0792 - accuracy: 1.0000\n",
      "Epoch 995/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0791 - accuracy: 1.0000\n",
      "Epoch 996/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0790 - accuracy: 1.0000\n",
      "Epoch 997/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0790 - accuracy: 1.0000\n",
      "Epoch 998/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0789 - accuracy: 1.0000\n",
      "Epoch 999/1000\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0789 - accuracy: 1.0000\n",
      "Epoch 1000/1000\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0788 - accuracy: 1.0000\n",
      "Weights= [[0.47313914]\n",
      " [0.43078765]] \n",
      "biases= [-2.0052295]\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0802 - accuracy: 1.0000\n",
      "loss:0.0802 accuracy:1.0000\n"
     ]
    }
   ],
   "source": [
    "# model.fit(X_train, Y_train, batch_size=n, epochs=1000, verbose=1, validation_data=(X_test, Y_test))\n",
    "model.fit(X_train, Y_train, batch_size=len(Y_train), epochs=1000, verbose=1)\n",
    "W, b = model.layers[0].get_weights()\n",
    "print('Weights=', W, '\\nbiases=', b)\n",
    "\n",
    "loss, accuracy = model.evaluate(X_test, Y_test, verbose=1)\n",
    "print('loss:%.4f accuracy:%.4f' % (loss, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.47313914]\n",
      " [0.43078765]]\n",
      "[0.47313914]\n",
      "[0.43078765]\n",
      "0.47313914\n",
      "0.43078765\n"
     ]
    }
   ],
   "source": [
    "print(W)\n",
    "print(W[0])\n",
    "print(W[1])\n",
    "print(W[0][0])\n",
    "print(W[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cc8c3a0910>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w1 = W[0][0]\n",
    "w2 = W[1][0]\n",
    "x = np.linspace(-10, 10, 101)\n",
    "y = (-b - w1*x) / w2\n",
    "plt.plot(X_train[:, 0], X_train[:, 1], '.')\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(file+'.h5', overwrite=True)  # 保存模型\n",
    "model.save(file+'_'+time.strftime(\"%Y%m%d-%H%M%S\", time.localtime())+'.h5')  # 再保存一遍，加上时间"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
